Site-Specific Beam Alignment in 6G via Deep Learning (2403.16186v1)
Abstract: Beam alignment (BA) in modern millimeter wave standards such as 5G NR and WiGig (802.11ay) is based on exhaustive and/or hierarchical beam searches over pre-defined codebooks of wide and narrow beams. This approach is slow and bandwidth/power-intensive, and is a considerable hindrance to the wide deployment of millimeter wave bands. A new approach is needed as we move towards 6G. BA is a promising use case for deep learning (DL) in the 6G air interface, offering the possibility of automated custom tuning of the BA procedure for each cell based on its unique propagation environment and user equipment (UE) location patterns. We overview and advocate for such an approach in this paper, which we term site-specific beam alignment (SSBA). SSBA largely eliminates wasteful searches and allows UEs to be found much more quickly and reliably, without many of the drawbacks of other machine learning-aided approaches. We first overview and demonstrate new results on SSBA, then identify the key open challenges facing SSBA.
- M. Qurratulain Khan, A. Gaber, P. Schulz, and G. Fettweis, “Machine learning for millimeter wave and terahertz beam management: A survey and open challenges,” IEEE Access, vol. 11, pp. 11880–11902, Feb. 2023.
- K. Ma, Z. Wang, W. Tian, S. Chen, and L. Hanzo, “Deep learning for mmWave beam-management: State-of-the-art, opportunities and challenges,” IEEE Wireless Commun., pp. 1–8, Aug. 2022.
- Y. Heng, J. G. Andrews, J. Mo, V. Va, A. Ali, B. L. Ng, and J. C. Zhang, “Six key challenges for beam management in 5.5G and 6G systems,” IEEE Commun. Mag., vol. 59, pp. 74–79, July 2021.
- R. W. Heath, N. Gonzalez-Prelcic, S. Rangan, W. Roh, and A. M. Sayeed, “An overview of signal processing techniques for millimeter wave MIMO systems,” IEEE J. Sel. Topics Signal Process., vol. 10, pp. 436–453, Feb. 2016.
- X. Li and A. Alkhateeb, “Deep learning for direct hybrid precoding in millimeter wave massive MIMO systems,” in Proc. IEEE Asilomar, pp. 800–805, Nov. 2019.
- Y. Heng, J. Mo, and J. G. Andrews, “Learning site-specific probing beams for fast mmWave beam alignment,” IEEE Trans. Wireless Commun., vol. 21, pp. 5785–5800, Jan. 2022.
- Y. Heng and J. G. Andrews, “Grid-free MIMO beam alignment through site-specific deep learning,” IEEE Trans. Wireless Commun., May 2023. early access.
- A. Alkhateeb, “DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications,” in Proc. Inf. Theory and Appl. Workshop (ITA), pp. 1–8, Feb. 2019.
- A. Alkhateeb, S. Jiang, and G. Charan, “Real-time digital twins: Vision and research directions for 6G and beyond,” arXiv preprint arXiv: 2301.11283, 2023.
- T. Mostak, “Introducing HeavyRF: Accelerated Cell Site Planning for Telcos.” Accessed Jul. 27, 2023. [Online]. Available: https://www.heavy.ai/blog/introducing-heavyrf-accelerated-cell-site-planning-for-telcos.
- Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. Dillon, B. Lakshminarayanan, and J. Snoek, “Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift,” in Proc. NeurIPS, vol. 32, 2019.
- F. Zenke, B. Poole, and S. Ganguli, “Continual Learning Through Synaptic Intelligence,” in Proc. ICML, vol. 70, pp. 3987–3995, Aug. 2017.
- F. Sohrabi, T. Jiang, W. Cui, and W. Yu, “Active sensing for communications by learning,” IEEE Journal on Sel. Areas in Communications, vol. 40, pp. 1780–1794, Mar. 2022.
- S. Liu, E. Johns, and A. J. Davison, “End-to-end multi-task learning with attention,” in Proc. IEEE/CVF CVPR, pp. 1871–1880, June 2019.
- A. M. Elbir, A. K. Papazafeiropoulos, and S. Chatzinotas, “Federated learning for physical layer design,” IEEE Commun. Mag., vol. 59, pp. 81–87, Nov. 2021.